""":
Deep Learning Assignment 3
Conditional GAN Skeleton Code.
Adopted from public sources, customized and improved for this assignment.
"""
#import necessary modules
import torch
import torch.nn as nn
from torchvision import transforms, datasets
from torch import optim as optim
# for visualization
from matplotlib import pyplot as plt
import math
import numpy as np
# tells PyTorch to use an NVIDIA GPU, if one is available.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# loading the dataset
training_parameters = {
"img_size": 28,
"n_epochs": 24, #24
"batch_size": 64,
"learning_rate_generator": 0.0002,
"learning_rate_discriminator": 0.0002,
}
# define a transform to 1) scale the images and 2) convert them into tensors
transform = transforms.Compose([
transforms.Resize(training_parameters['img_size']), # scales the smaller edge of the image to have this size
transforms.ToTensor(),
])
# load the dataset
train_loader = torch.utils.data.DataLoader(
datasets.FashionMNIST(
'./data', # specifies the directory to download the datafiles to, relative to the location of the notebook.
train = True,
download = True,
transform = transform),
batch_size = training_parameters["batch_size"],
shuffle=True
)
# Fashion MNIST has 10 classes, just like MNIST. Here's what they correspond to:
label_descriptions = {
0: 'T-shirt/top',
1 : 'Trouser',
2 : 'Pullover',
3 : 'Dress',
4 : 'Coat',
5 : 'Sandal',
6 : 'Shirt',
7 : 'Sneaker',
8 : 'Bag',
9 : 'Ankle boot'
}
# Create the Generator model class, which will be used to initialize the generator
class Generator(nn.Module):
def __init__(self, input_dim, output_dim, num_labels=10): # to initialize the model-wide parameters. When you run `generator = Generator(params)`, those "params" are passed to __init__.
super(Generator,self).__init__() # initialize the parent class
# TODO (5.4) Turn this Generator into a Conditional Generator by
# 1. Adjusting the input dimension of the first hidden layer.
# 2. Modifying the input to the first hidden layer in the forward class.
self.label_embedding = nn.Embedding(10, 10) # This function will be useful.
self.hidden_layer1 = nn.Sequential(
nn.Linear(input_dim+10, 256),
nn.LeakyReLU(0.2)
)
self.hidden_layer2 = nn.Sequential(
nn.Linear(256, 512),
nn.LeakyReLU(0.2)
)
self.hidden_layer3 = nn.Sequential(
nn.Linear(512, 1024),
nn.LeakyReLU(0.2)
)
self.hidden_layer4 = nn.Sequential(
nn.Linear(1024, output_dim),
nn.Tanh()
)
def forward(self, x, labels):
c = self.label_embedding(labels)
x = torch.cat([x, c], 1)
output = self.hidden_layer1(x)
output = self.hidden_layer2(output)
output = self.hidden_layer3(output)
output = self.hidden_layer4(output)
return output.to(device)
class Discriminator(nn.Module):
def __init__(self, input_dim, output_dim=1, num_labels=None):
super(Discriminator, self).__init__()
self.label_embedding = nn.Embedding(10, 10)
# TODO (5.4) Modify this discriminator to function as a conditional discriminator.
self.hidden_layer1 = nn.Sequential(
nn.Linear(input_dim+10, 1024),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden_layer2 = nn.Sequential(
nn.Linear(1024, 512),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden_layer3 = nn.Sequential(
nn.Linear(512, 256),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden_layer4 = nn.Sequential(
nn.Linear(256, output_dim),
nn.Sigmoid()
)
def forward(self, x, labels=None): # labels to be used in 5.4.
c = self.label_embedding(labels)
x = torch.cat([x, c], 1)
output = self.hidden_layer1(x)
output = self.hidden_layer2(output)
output = self.hidden_layer3(output)
output = self.hidden_layer4(output)
return output.to(device)
discriminator = Discriminator(784,1).to(device) # initialize both models, and load them to the GPU or CPU.
generator = Generator(100,784).to(device)
discriminator_optimizer = optim.Adam(discriminator.parameters(), lr=training_parameters['learning_rate_discriminator'])
generator_optimizer = optim.Adam(generator.parameters(), lr=training_parameters['learning_rate_generator'])
# Establish convention for real and fake labels during training
real_label = 1.
fake_label = 0.
#Loss_D - discriminator loss calculated as the sum of losses for the all real and all fake batches $(\log (D(x))+\log (1- D(G(z))))
loss_func = nn.BCELoss() # Binary Cross Entropy Loss
def train_generator(batch_size):
"""
Performs a training step on the generator by
1. Generating fake images from random noise.
2. Running the discriminator on the fake images.
3. Computing loss on the result.
:arg batch_size: the number of training examples in the current batch
Returns the average generator loss over the batch.
"""
# Start by zeroing the gradients of the optimizer
generator_optimizer.zero_grad()
# 1. Create a new batch of fake images (since the discriminator has just been trained on the old ones)
noise = torch.randn(batch_size,100).to(device) # whenever you create new variables for the model to process, send them to the device, like this.
generated_labels = torch.randint(0, 10, (batch_size,)).to(device)
generator_output = generator(noise, labels = generated_labels)
# 2. Run the discriminator on the fake images
discriminator_output = discriminator(generator_output, labels = generated_labels)
###----copied----
real_label_vector = torch.full((batch_size,), real_label, dtype=torch.float, device=device)
real_label_vector = real_label_vector.view(-1, 1)
#-------
# 3. Compute the loss
loss = loss_func(discriminator_output, real_label_vector)
loss.backward()
generator_optimizer.step()
loss = loss.mean().item()
return loss
def train_discriminator(batch_size, images, labels=None): # labels to be used in 5.4.
"""
Performs a training step on the discriminator by
1. Generating fake images from random noise.
2. Running the discriminator on the fake images.
3. Running the discriminator on the real images
3. Computing loss on the results.
:arg batch_size: the number of training examples in the current batch
:arg images: the current batch of images, a tensor of size BATCH x 1 x 64 x 64
:arg labels: the labels corresponding to images, a tensor of size BATCH
Returns the average loss over the batch.
"""
discriminator_optimizer.zero_grad()
###----fake images----###
# 1. Create a new batch of fake images (since the discriminator has just been trained on the old ones)
noise = torch.randn(batch_size,100).to(device) # whenever you create new variables for the model to process, send them to the device, like this.
generated_labels = torch.randint(0, 10, (batch_size,)).to(device)
generator_output = generator(noise, labels = generated_labels)
# 2. Run the discriminator on the fake images
discriminator_output = discriminator(generator_output, labels = generated_labels)
# 3. Compute the loss
fake_label_vector = torch.full((batch_size,), fake_label, dtype=torch.float, device=device)
fake_label_vector = fake_label_vector.view(-1, 1)
loss_fake = loss_func(discriminator_output, fake_label_vector)
###----real images----###
# 1. Run the discriminator on the real images
images = torch.flatten(images, start_dim=1)
discriminator_output = discriminator(images, labels = labels)
# 2. Compute the loss
real_label_vector = torch.full((batch_size,), real_label, dtype=torch.float, device=device)
real_label_vector = real_label_vector.view(-1, 1)
loss_real = loss_func(discriminator_output, real_label_vector)
#combine losses
loss = loss_real + loss_fake
loss.backward()
discriminator_optimizer.step()
loss = loss.mean().item()
return loss
for epoch in range(training_parameters['n_epochs']):
G_loss = [] # for plotting the losses over time
D_loss = []
for batch, (imgs, labels) in enumerate(train_loader):
batch_size = labels.shape[0] # if the batch size doesn't evenly divide the dataset length, this may change on the last epoch.
#generator first training
lossG = train_generator(batch_size)
G_loss.append(lossG)
#single discriminator training
lossD = train_discriminator(batch_size, imgs, labels)
D_loss.append(lossD)
#generator second training
lossG = train_generator(batch_size)
G_loss.append(lossG)
if ((batch + 1) % 500 == 0 and (epoch + 1) % 1 == 0):
# Display a batch of generated images and print the loss
print("Training Steps Completed: ", batch)
with torch.no_grad(): # disables gradient computation to speed things up
noise = torch.randn(batch_size, 100).to(device)
fake_labels = torch.randint(0, 10, (batch_size,)).to(device)
generated_data = generator(noise, fake_labels).cpu().view(batch_size, 28, 28)
# display generated images
batch_sqrt = int(training_parameters['batch_size'] ** 0.5)
fig, ax = plt.subplots(batch_sqrt, batch_sqrt, figsize=(15, 15))
for i, x in enumerate(generated_data):
ax[math.floor(i / batch_sqrt)][i % batch_sqrt].set_title(label_descriptions[int(fake_labels[i].item())])
ax[math.floor(i / batch_sqrt)][i % batch_sqrt].imshow(x.detach().numpy(), interpolation='nearest', cmap='gray')
ax[math.floor(i / batch_sqrt)][i % batch_sqrt].get_xaxis().set_visible(False)
ax[math.floor(i / batch_sqrt)][i % batch_sqrt].get_yaxis().set_visible(False)
plt.show()
#fig.savefig(f"./results/CGAN_Generations_Epoch_{epoch}")
#fig.savefig(f"pset/pset3/results/CGAN_Generations_Epoch_{epoch}")
fig.savefig(f"CGAN_Generations_Epoch_{epoch}")
print(
f"Epoch {epoch}: loss_d: {torch.mean(torch.FloatTensor(D_loss))}, loss_g: {torch.mean(torch.FloatTensor(G_loss))}")
Training Steps Completed: 499
Epoch 0: loss_d: 1.4205117225646973, loss_g: 0.8135586977005005 Training Steps Completed: 499
Epoch 1: loss_d: 1.4017736911773682, loss_g: 0.7450965046882629 Training Steps Completed: 499
Epoch 2: loss_d: 1.4091084003448486, loss_g: 0.7506999969482422 Training Steps Completed: 499
Epoch 3: loss_d: 1.3866671323776245, loss_g: 0.7329487204551697 Training Steps Completed: 499
Epoch 4: loss_d: 1.3881326913833618, loss_g: 0.7022793292999268 Training Steps Completed: 499
Epoch 5: loss_d: 1.3888123035430908, loss_g: 0.6981258392333984 Training Steps Completed: 499
Epoch 6: loss_d: 1.3961634635925293, loss_g: 0.7504462599754333 Training Steps Completed: 499
Epoch 7: loss_d: 1.3926175832748413, loss_g: 0.7233808636665344 Training Steps Completed: 499
Epoch 8: loss_d: 1.4169260263442993, loss_g: 0.7454743385314941 Training Steps Completed: 499
Epoch 9: loss_d: 1.3897982835769653, loss_g: 0.7014853358268738 Training Steps Completed: 499
Epoch 10: loss_d: 1.398500919342041, loss_g: 0.8133668303489685 Training Steps Completed: 499
Epoch 11: loss_d: 1.395297646522522, loss_g: 0.6796686053276062 Training Steps Completed: 499
Epoch 12: loss_d: 1.390921711921692, loss_g: 0.6977830529212952 Training Steps Completed: 499
Epoch 13: loss_d: 1.4131816625595093, loss_g: 0.7748185992240906 Training Steps Completed: 499
Epoch 14: loss_d: 1.391539216041565, loss_g: 0.6985052227973938 Training Steps Completed: 499
Epoch 15: loss_d: 1.3887841701507568, loss_g: 0.6978585124015808 Training Steps Completed: 499
Epoch 16: loss_d: 1.393417239189148, loss_g: 0.6952545046806335 Training Steps Completed: 499
Epoch 17: loss_d: 1.3918980360031128, loss_g: 0.7024227976799011 Training Steps Completed: 499
Epoch 18: loss_d: 1.3913910388946533, loss_g: 0.7016594409942627 Training Steps Completed: 499
Epoch 19: loss_d: 1.3902037143707275, loss_g: 0.6938185095787048 Training Steps Completed: 499
Epoch 20: loss_d: 1.3925540447235107, loss_g: 0.7033031582832336 Training Steps Completed: 499
Epoch 21: loss_d: 1.3885912895202637, loss_g: 0.6999940872192383 Training Steps Completed: 499
Epoch 22: loss_d: 1.3965940475463867, loss_g: 0.7255759239196777 Training Steps Completed: 499
Epoch 23: loss_d: 1.3893269300460815, loss_g: 0.7030029296875
#save the model
torch.save(generator, 'generator_cond_double.pth')
torch.save(discriminator, 'discriminator_cond_double.pth')